%2multibyte Version: 5.50.0.2890 CodePage: 1252
%\bibliographystyle{agsm}


\documentclass[12pt]{article}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\usepackage{eurosym}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amscd}
\usepackage{amsthm}
\usepackage{amssymb}
\usepackage[active]{srcltx}
\usepackage{setspace}
\usepackage{graphicx}
\usepackage{subfig}
\usepackage{natbib}
\usepackage{marginnote}
\usepackage{color}

\setcounter{MaxMatrixCols}{10}
%TCIDATA{OutputFilter=LATEX.DLL}
%TCIDATA{Version=5.50.0.2890}
%TCIDATA{Codepage=1252}
%TCIDATA{<META NAME="SaveForMode" CONTENT="1">}
%TCIDATA{BibliographyScheme=BibTeX}
%TCIDATA{LastRevised=Monday, July 15, 2013 16:11:13}
%TCIDATA{<META NAME="GraphicsSave" CONTENT="32">}
%TCIDATA{Language=American English}

\renewcommand{\baselinestretch}{1.62}
\setlength{\oddsidemargin}{0pt}
\setlength{\evensidemargin}{0pt}
\setlength{\headheight}{0pt}
\setlength{\topmargin}{0pt} 
\setlength{\textheight}{8.5in}
\setlength{\textwidth}{6.5in}
%\input{tcilatex}
\begin{document}

\title{\textbf{Local Employment Impact from Competing Energy Sources: Shale
Gas versus Wind Generation in Texas}\thanks{
Preliminary draft. Comments are welcome.}}
\author{Peter Hartley\thanks{
Department of Economics, Rice University and Business School, University of
Western Australia} \and Kenneth B. Medlock III\thanks{
James A. Baker III Institute for Public Policy, Rice University} \and Ted
Temzelides\thanks{
Department of Economics, Rice University} \and Xinya Zhang\thanks{
Department of Economics, Rice University}}
\maketitle

\pagenumbering{arabic} \clearpage

\clearpage

\begin{center}
\textbf{Abstract}
\end{center}

\noindent The rapid development of both wind power and of shale gas has been
receiving significant attention both in the media and among policy makers.
Since these are competing sources of electricity generation, it is
informative to investigate their relative merits regarding job creation. We
use a panel econometric model to estimate the historical job-creating
performance of wind versus that of shale oil and gas. The model is estimated
using monthly county level data from Texas from 2001 to 2011. Both
first-difference and GMM methods show that shale-related activity has
brought strong employment to Texas: 77 short-term jobs or 6.4 full-time
equivalent (FTE) jobs per well. Given that 5482 new directional/fractured
wells were drilled in Texas in 2011, this implies that about 35000 FTE jobs
were created in that year alone. We did not, however, find a corresponding
impact on wages. Our estimations did not identify a non-negligible impact
from the wind industry on either employment or wages. \noindent \emph{\ }%
\newpage

\section{Introduction}

\label{sec:introduction}

Following the dramatic increase in natural gas supplies, and decrease in
natural gas prices, as result of the development of shale gas, there is an
ongoing discussion about the relative merits of renewable energy versus
those of natural gas. Proponents of renewable energy emphasize its potential
to reduce CO$_{2}$ emissions. However, they also point to other benefits
including especially the potential of renewable energy to increase
employment opportunities and drive economic growth. The natural gas industry
has commented much less on the employment effects of shale gas production,
but a review of the employment experience of different states over the last
recession suggests that the effects may be non-negligible. We examine this
experience in more detail using detailed microeconomic data from the state
of Texas, which has seen substantial development of both shale and wind
energy.

Since energy produced through renewable sources is still more expensive than
that produced through fossil fuels, governments around the world have been
providing tens of millions of dollars in subsidies to the renewable
industry. More than half of all states in the U.S. have established
Renewable Portfolio Standards to promote electricity generation from
renewable sources.\footnote{%
Renewable portfolio standards (RPS), also referred to as renewable
electricity standards (RES), require or encourage electricity producers
within a given jurisdiction to supply a certain minimum share of electricity
from designated renewable resources.} Federal production tax credits and
grants also contributed to increases in renewable capacity and generation
between 2001 and 2011. Partly as a result, the renewable energy sector has
developed rapidly in the past 12 years. In particular, since wind generation
is currently the most competitive non-hydroelectric renewable source, as
Figure~\ref{fig:renewElec} shows it has grown rapidly in recent years. Other
sources of non-hydroelectric renewable electricity generation, including
biomass, geothermal, and wood, have remained relatively stable since 2000.%
\footnote{%
In 2011, in the United States, biomass accounted for about 11\% of the total
renewable electricity generation, wind accounted for 23\%, solar
(photovoltaics and concentrating solar power) accounted for 1\%, and
geothermal for 3\%.} 
\begin{figure}[h]
\centering \includegraphics[width=0.6\textwidth]{renewElec.jpg}
\caption[Non hydro-power renewable energy generation, 1990-2011]{Non
hydro-power renewable energy generation, 1990-2011. Data source: EIA}
\label{fig:renewElec}
\end{figure}

While economists concentrate on objectives related to economic efficiency
and growth rather than job creation, policy makers often emphasize the
job-creating potential of renewable energy sources. In that regard, wind
energy production, and especially the construction and installation of
physical plants and facilities, has the potential to increase domestic
employment. Indeed, domestic wind-turbine and component manufacturing
capacity has increased recently. Eight of the ten wind turbine manufacturers
with the largest share of the U.S. market in 2011 had at least one
manufacturing facility in the United States at the end of 2011. By contrast,
in 2004 only one utility-scale wind-turbine manufacturer (GE) assembled
nacelles in the United States.\footnote{%
See 2011 Wind Technology Market Report by the U.S. Department of Energy.} In
addition, both foreign and domestic firms announced or opened new wind
turbine and component manufacturing facilities in 2011. The American Wind
Energy Association (AWEA) estimates that the entire wind energy sector
directly and indirectly employed 75,000 full-time workers in the United
States at the end of 2011.

At the same time as wind capacity has been expanding, oil and gas companies
have demonstrated that the vast resources of shale gas and oil in North
America can be exploited at reasonable cost. This ``shale revolution'' is a
result of cost-effective technological developments such as horizontal
drilling and hydraulic fracturing. The combination of these techniques
caused U.S. production of shale oil and gas to boom.

From the perspective of competition with wind, we are most interested in the
increase in natural gas production since very little oil is now used to
generate electricity. The Energy Information Administration's 2012 Annual
Energy Outlook \citep{EIA2012} projects that the share of shale gas in total
U.S. natural gas production will increase from 4 percent in 2005 to 34
percent by 2015 and 49 percent by 2025. As shown in Figure~\ref{fig:NGProd},
shale gas is the largest contributor to natural gas production growth; there
is relatively little change in production levels from tight formations,
coal-bed methane deposits, and offshore fields. 
\begin{figure}[h]
\centering \includegraphics[width=0.6\textwidth]{NGprod.jpg}
\caption[Natural gas production by source, 1990-2035]{Natural gas production
by source, 1990-2035 (TCF). Data source: EIA}
\label{fig:NGProd}
\end{figure}

The development of shale oil and gas resources has created an investment
boom in the oil and gas industry and led to economic revitalization in
places like North Dakota, Texas, Alberta, West Pennsylvania, and Louisiana
to name a few. During 2007-2011, employment in the oil and gas extraction
sector grew at an annual rate of 6.28 percent, or, 27.58 percent in total.
By comparison, during the same period, total employment declined 3.40
percent below its starting value (Figure~\ref{fig:ogejobs}). Meanwhile,
there is anecdotal evidence that states with more shale oil and gas
production have experienced increased employment, while the nationwide
employment growth rate remains negative (Figure~\ref{fig:statejobs}).
Furthermore, the relatively low prices resulting from the expanded natural
gas supply are stimulating downstream investment in manufacturing,\footnote{%
This is especially relevant for those sectors that are sensitive to energy
costs, such as basic chemicals, plastics \& rubber, pharmaceuticals,
aluminum, pesticides, paints, and fertilizers.} as well as in electricity
generation (Figure~\ref{fig:TotalElec}) and in transportation. 
\begin{figure}[h]
\centering
\subfloat[]{\label{fig:ogejobs}\includegraphics[width=0.5%
\textwidth]{ogejobs.png}} \subfloat[]{\label{fig:statejobs}%
\includegraphics[width=0.5\textwidth]{statejobs.png}}
\caption{Oil and gas extraction employment, 2007-2011}
\label{fig:shalejobs}
\end{figure}

\begin{figure}[h]
\centering \includegraphics[width=0.9\textwidth]{summercap.pdf}
\caption[Electricity net summer capacity by source]{Electricity net summer
capacity by source (all sectors), 1949-2011. Data source: EIA}
\label{fig:TotalElec}
\end{figure}
While the aggregate effect on employment from developing different energy
sources is an important question, it cannot be readily answered in the
context of traditional macroeconomic models. As these models assume market
clearing, they cannot account for variations in unemployment rates and,
thus, are not well suited to study the employment consequences of
alternative government policies or other shocks.

Generally speaking, there are two existing approaches to analyzing the
employment impacts of the energy industry. The first uses an input-output
(I/O) model. The second approach is based on survey responses from
employers, and uses simple descriptive techniques.\footnote{%
See Section~\ref{sec:literature-review} for a detailed discussion.} In this
study, we collect data on the historical job creation per unit of energy
produced by each energy source. We then use an econometric model to estimate
the historical job-creating performance of wind versus that of shale gas.
Like the survey approach, our analysis focuses on localized employment
effects rather than more distant impacts. However, the econometric analysis
allows us to compare the employment impacts in a more systematic and
consistent way across the two energy sources.

\section{Literature Review}

\label{sec:literature-review} In the last few years, a large number of
reports have emerged studying employment in the shale and wind industries.
While non-government organizations and consulting firms conducted most of
these studies, a few peer-reviewed journal articles have also been published.

Generally speaking, the studies have been of two types. The first uses the
input-output (I/O) model. The second approach is based on survey responses
from employers.

Th I/O model focuses on the use of various inputs to production and how the
goods and services produced are allocated between various industrial sectors
and consumers. I/O models attempt to account for the economy as a whole.
They capture employment multiplier effects, as well as the macroeconomic
impacts of shifts between sectors. Hence they could account for losses in
one sector (e.g.,\ conventional oil industry) created by the growth in
another sector (e.g.,\ wind energy ). One drawback is that collecting data
for an I/O model is highly labor-intensive. As a result, the calibration
process for default multiplier parameters may be biased due to lack of
information.

Two widely used I/O studies of the oil and gas industry are the IMPLAN model
(see \citet{IHS2012}, \citet{UTSA2012}, \citet{PennState2009}, and %
\citet{ACC2011}) and the RIMS II model, used by the U.S. Bureau of Economic
Analysis (BEA) (see \citet{HaynesShale2009}).\footnote{%
The IMPLAN model uses a national input-output dollar flow table called the
Social Accounting Matrix (SAM) to model the way a dollar injected into one
sector is spent and re-spent in other sectors of the economy. RIMS II
provides I/O multipliers that measure the effects on output, employment, and
earnings from any changes in a region's industrial activity.} These studies
typically find significant positive effects from the shale oil and gas
sectors on jobs, income, and economic growth.

A study of the Eagle Ford Shale \citep{UTSA2012} estimates that in 2011
shale activity raised output in the local (14-county) region by just under
\$20 billion dollars and supported 38,000 full-time jobs. If the region is
extended to 20 counties, the study finds that 47,097 full-time jobs were
supported. A nationwide shale industry report \citep{IHS2012} has found that
the shale gas industry supported 600,000 jobs in 2010. This is projected to
grow to nearly 870,000 in 2015, and to over 1.6 million by 2035. Two reports
on the Marcellus shale by Pennsylvania State University \citep{PennState2009}
and by West Virginia University \citep{WVU2009} show that the oil and gas
industry in Pennsylvania generated \$3.8 billion in value added, and over
48,000 jobs in 2009. In West Virginia, the economic impact of the oil and
natural gas industry in 2009 was estimated to be \$3.1 billion in total
value added, while approximately 24,400 jobs were created.

The Jobs and Economic Development Impact (JEDI) model developed by the
National Renewable Energy Laboratory (NREL) is a series of spreadsheet-based
I/O models that estimate the economic impacts of constructing and operating
power plants, fuel production facilities, and other projects at the local
(usually state) level. \citet{TCU2009} validate (or test) the JEDI Wind
Energy Model using data from NextEra's Capricorn Ridge and Horse Hollow
facilities. They then used it to examine the economic impact of large-scale
wind-farm construction. They find that the JEDI model overestimates local
jobs during the construction phase in smaller, rural counties, and that it
underestimates by more than 50\% the number of jobs in large, urban
counties. This is because the JEDI model sets the local share of employment
attributable to wind to be the same for all counties. In addition, the JEDI
model assumes 100\% local share for operations and maintenance (O\&M) jobs,
which might be implausible, especially in small rural counties.

An alternative approach to I/O models uses ``bottom up'' estimates based on
industry/utility surveys of project developers and equipment manufacturers,
and on primary employment data from companies across manufacturing,
construction, installation, and O\&M. For wind energy, most reports are
analytical-based studies, and only calculate direct employment impacts. As
an example, a case study on the economic effects of the Gulf wind project in
Texas reports the estimated creation of 250 - 300 jobs during the peak
construction period (9 months), and 15 - 20 permanent jobs.\footnote{%
Gulf Wind: Harnessing the Wind for South Texas}

A report on the wind industry from the Natural Resources Defense Council
(NRDC) measures the number of direct jobs that a typical wind farm may
create across the entire value chain. They analyze each of the 14 key
value-chain activities independently to determine the number of workers
involved at each step in building the wind farm. They find that a typical
wind farm of 250MW would create 1079 jobs over the lifetime of the project.
Similarly, the Renewable Energy Policy Project (REPP) has developed a
spreadsheet-based model using data based on a survey of current industry
practices. They use it to calculate the number of direct jobs from wind,
solar photovoltaic, biomass, and geothermal activities that would result
from the enactment of a Renewable Portfolio Standard. They find that every
100 MW of wind power installed creates 475 jobs in total (313 manufacturing
jobs, 67 installation jobs, and 95 jobs in O\&M).

\section{Data}

\label{sec:data} We use data from the state of Texas. Texas has rich shale
gas and oil resources and, at the same time, it is the national leader in
wind installations and a manufacturing hub for the wind energy industry.
According to EIA, Texas accounted for 40 percent of U.S. marketed dry shale
gas production in 2011, making it the leading unconventional gas producer in
the U.S. Meanwhile, Texas leads the nation in wind-powered generation
capacity and it is the first state to have reached 10,000 megawatts of wind
capacity.

In Texas, there are 254 counties.\footnote{%
Out of these, 77 are urban counties.} For each county $i=1,\ldots ,254$, we
have collected observations for $T=132$ months, or a total of $11$ years ($%
2001$ - $2011$), making the panel balanced.

We use total employment in all industries as one dependent variable. We did
not restrict the data to specific industries since we want to measure total
employment effects, including indirect job-creation. This includes jobs
created in upstream and infrastructure supplying industries, as well as
induced jobs, such as jobs added in sectors supplying consumer items (food,
auto, housing, etc.) and services. Another dependent variable of interest is
the average weekly wage since, unless the supply of labor is perfectly
elastic, it should also be impacted by an increase in the demand for
workers. We use monthly employment data and quarterly wage data from the
Quarterly Census of Employment and Wages (QCEW) Database of the Bureau of
Labor Statistics (BLS).\footnote{%
The QCEW employment and wage data is derived from micro data summaries of
9.1 million employer reports of employment and wages submitted by states to
the BLS in 2011. These reports are based on place of employment rather than
place of residence. Average weekly wage values are calculated by dividing
quarterly total wages by the average of the three monthly employment levels
and then dividing the result by 13, as there are 13 weeks in a quarter.} The
latter has been adjusted to a real wage using the implicit price GDP
deflator (IPD) from BEA.\footnote{%
The implicit price GDP deflator is the ratio of the current-dollar value of
GDP to its corresponding chained-dollar value multiplied by 100.}

In order to evaluate the impact from shale and wind development on
employment and on the local economy, we need to measure activity in the
shale and wind industries. The key explanatory variables we use are the
number of unconventional wells completed and the newly installed wind
capacity in each county and each month, respectively.

Of course, other variables could also be used to reflect other aspects of
new activity in the shale industry. These include the number of permits
issued, changes in rig counts, the number of wells spudded, and the change
in total shale gas production. We chose the number of wells completed
because the well completion date indicates the end of the construction
period for each well. We suspect that more direct and on-site jobs are
created during that period. To fully describe the impact of shale on
employment, especially the multiplier effects on job creation in the local
economy, we allow well drilling activities to affect employment with a lag,
and we study both pre-completion and post-construction effects.

\begin{figure}[h]
\centering \includegraphics[width=0.9\textwidth]{wells.pdf}
\caption{Number of completed directional-drilled/fractured oil and gas wells
per county per year, Jan, 2001 - Dec, 2011}
\label{fig:newwells}
\end{figure}

In the shale industry, the entire process from spudding to producing
marketed output can take up to 3-4 months. Horizontal drilling itself
currently takes approximately 18-25 days from start to finish. Then wells
are fractured to release the gas or oil before the well is completed. The
well is then connected to a processing facility and pipelines, which
transport the products to market. Among these activities, hydraulic
fracturing and completion are the most labor intensive. Hence, we expect
``number of wells completed'' to have a peak impact on employment in the
month of well completion.

We use the Drilling Info Database for information about oil and gas wells.
We concentrate our study on wells that are both directional/horizontally
drilled and hydraulically fractured.\footnote{%
This filter option is only available for Texas data.} Thus, we exclude
conventional oil/gas wells from our data set. There were 31050
directional/horizontal and fractured wells completed in 174 Texas counties
during 2001 - 2011, including 25467 gas wells, 4963 oil wells, and 620 wells
classified as ``other types.'' Figure \ref{fig:newwells} indicates that shale gas
and oil developed very quickly in the past 12 years, from 1 well per month
in Jan, 2001 to around 500 in 2011. The completion date and location of each
well are used to count the number of wells completed in each county each
month.

To measure wind activity in each county we use the installed nameplate
capacity brought online in each month. We do not use the change in power
generated\footnote{%
Although this would represent the change in monthly wind velocity
distribution in addition to the number of generators, it could be argued
that wear and tear, and thus the need for maintenance, is likely to be a
function of power generated.} as more jobs are created during the
construction period than during the O\&M period. The installed capacity and
online year for new wind projects in Texas throughout the period 2007-2011
is taken from the American Wind Energy Association (AWEA). For wind projects
before 2007, we used EIA electricity data on plant level output and a wind
industry progress report by \textit{Wind Today}. To find the online month
and county location for each new wind project, we needed to refer to
additional sources, such as project websites or local news stories. For wind
farms covering several neighboring counties, we divided newly installed
capacity equally between each of the counties involved. Until 2011, we found
that 125 wind projects had been constructed in 40 counties, with a total
installed capacity of 10006MW (compared to 6 counties and 920MW in 2001).

\begin{figure}[h]
\centering \includegraphics[width=0.9\textwidth]{windcap.pdf}
\caption{New wind capacity installed during Jan, 2001 - Dec, 2011}
\label{wells}
\end{figure}

Since regression test statistics do not have the usual asymptotic
distributions when variables are non-stationary, before undertaking
regression analysis we investigated the stationarity properties of our
variables. To test for non-stationarity in our panel data setting, we
consider the following model written in difference form: 
\begin{equation}
\Delta y_{it}=\rho y_{i,t-1}+\sum_{L=1}^{p_{i}}\delta _{i}\Delta
y_{i,t-L}+\alpha _{0}+\alpha _{1}t+u_{it},\;t=1,2,\ldots  \label{eq:ADF}
\end{equation}%
where $i$ indexes the county and $t$ the month. We then test the hypothesis
that $\rho =0$. Note that the term $\alpha _{0}+\alpha _{1}t$ allows for a
constant and deterministic time trend. When $\rho =0$, the series $y_{it}$
has a unit root and it is a random walk (the effect of any one shock lasts
forever). When $\rho <0$, $y_{t}$ is covariance stationary\footnote{%
The covariance between $y_{t}$ and $y_{t+h}$ only depends on $h$.} and the
correlation between variables declines as they get farther apart in time.

Since the $t$-statistic for testing $H_0:\rho=0$ does not have the usual
distribution, we use the Im, Pesaran, and Shin (IPS) test \citep{Im2003} to
test for $\rho =0$. This test is based on the estimation of the above
augmented Dickey-Fuller (ADF) regression for the time series in each county.
A statistic is then computed using the $t$-statistics associated with the
lagged variable. Note that this procedure does not require $\rho $ to be the
same for all counties. The null hypothesis is that all series have a unit
root, and the alternative is that some have a unit root while others have
different values of $\rho _{i}<0$. To run the test, we must first determine
the optimal number of lags, $p_{i}$, for each time series in the panel. If
we assume too few lags, $u_{it}$ will be serially correlated and the test
statistics will not have the assumed distribution. If we assume too many
lags, the power of the test statistic declines. Since we are working with
monthly data, we set the maximum $p_{i}$ at $14$, which is slightly larger
than an annual cycle. We then use both the Swartz information criteria (SIC)
and the Akaike information criteria (AIC) to determine the optimal value of $%
p_{i}$. For both the employment and the wage series, we found that the $p$%
-value of the IPS test is close to zero. Hence, $H_{0}$ is rejected and we
conclude that some counties may not have unit roots.

As a complement to the IPS test, we used a test based on \citet{Hadri2000}
that does not rely on the ADF regression. Like the KPSS %
\citep{Kwiatkowski1992} test, this takes as its null hypothesis that all
panels are stationary, while the alternative is that some panels contain
unit roots. The Hadri statistic is formed as the cross-sectional average of
the individual KPSS statistics, standardized by their asymptotic mean and
standard deviation. In our data set, the Hadri test rejects $H_{0}$ as well,
implying that at least one county has a unit root.

In summary, we conclude that the employment and wage series have unit roots
in some counties, while others are stationary. We then applied the
Dickey-Fuller Generalized Least Squares test (DF-GLS) (at 5\% level) and the
KPSS test (at 10\% level) to each county. We found that in 156 counties, the
DF-GLS test cannot reject a unit root, while the KPSS test shows the
presence of a unit root.\footnote{%
Among the 156 counties, 46 are urban. That is, employment in 60\% of the
urban counties and 62\% of the rural counties may have unit roots. On the
other hand, based on results from both tests, only 34 counties have
stationary employment series.}

\section{Econometric Issues}

\label{sec:model-specification}

We estimate a panel regression using data from all 254 counties in Texas
covering the years 2001-2011. Working with panel data allows us to study
dynamic relationships, which we cannot do using a single cross section. It
also allows us to test for the presence of specific effects in counties with
shale and wind activities versus those without. This mitigates a potential
problem with pure time series analysis, whereby many exogenous factors can
change at the same time, making it difficult to attribute an outcome to any
one particular change. The panel analysis presumes these other factors
affect all counties symmetrically. In addition, a panel data set also allows
us to control for unobserved heterogeneity across counties.

\subsection{Assumptions}

\label{sec:assumptions} We start with a static linear unobserved effects
model: 
\begin{equation}
y_{it}=\mathbf{x}_{it}\mathbf{\beta }+\theta _{t}+c_{i}+u_{it},t=1,2,\ldots
,T,  \label{eq:basic}
\end{equation}%
where $y_{it}$ is a scalar, $\mathbf{x}_{it}$ is a $1\times K$ vector for $%
t=1,2,\ldots ,T$, and $\mathbf{\beta }$ is a $K\times 1$ vector. Here, $%
c_{i} $ indicates a time-invariant unobservable county effect, and $\theta
_{t}$ represents a series of time fixed effects.

The first issue we need to address is whether the county effects $c_i$
should be taken as fixed or random. A Hausman test yields a statistic $%
\chi^{2}=601.67$ with a $p$-value close to zero, indicating that the random
effects approach is inconsistent. We therefore assume that the $c_i$ are
fixed.

To make the model more realistic, we allow for arbitrary dependence between
the unobserved effects, $c_{i}$, and the observed explanatory variables, $%
\mathbf{x_{it}}$. For example, underground geology characteristics would be
included in $c_{i}$, and these could be correlated with the number of wells
drilled in county $i$. Also, wind capacity highly depends on the climate,
and especially the wind resource of the county, which is also part of the
variable $c_{i}$.

With a fixed effects (FE) or first difference (FD) approach, the explanatory
variables are allowed to be arbitrarily correlated with $c_{i}$, but strict
exogeneity conditional on $c_{i}$ is still required. The assumption of
strict exogeneity, introduced by \citet{Chamberlain1982}, requires that 
\begin{equation}
E(u_{it}|\mathbf{x}_{i},c_{i})=0,t=1,2,\ldots ,T.  \label{eq:StrictE}
\end{equation}%
That is, once $\mathbf{x_{it}}$ and $c_{i}$ are accounted for, $\mathbf{%
x_{is}}$ has no partial effect on $y_{it}$, for $s\neq t$. In addition, $%
u_{it}$ has zero mean conditional on all explanatory variables in all time
periods. This is a stronger assumption than contemporaneous exogeneity,
which requires that $E(u_{it}|\mathbf{x_{it}},c_{i})=0$. In particular, the
latter assumption says nothing about the relationship between $\mathbf{x_{s}}
$ and $u_{t}$ for $s\neq t$. Sequential exogeneity, which requires that $%
E(u_{it}|\mathbf{x_{it},x_{i,t-1},\ldots ,x_{i1}},c_{i})=0$, for $%
t=1,2,\ldots ,T$, is stronger than contemporaneous exogeneity. It implies
that $\mathbf{x_{s}}$ is uncorrelated with $u_{t}$ for all $s\leq t$, but
imposes no constraints on the correlation between $\mathbf{x_{s}}$ and $u_{t}
$ for $s>t$. The pooled OLS estimator for $\mathbf{\beta }$ is consistent
only if the explanatory variables satisfy contemporaneous exogeneity and
zero correlation with the unobserved individual effects.

The idea behind the fixed effects approach is to transform the equations by
removing the intertemporal mean, thereby eliminating the unobserved effects.
One can then apply pooled OLS to get FE estimators. Similarly, the FD
approach transforms the equations by lagging the model one period and
subtracting, then applying pooled OLS to get FD estimators. As we mentioned
at the end of the previous section, we found that more than half of the
counties have highly persistent employment series. Using time series with a
unit root process in a regression equation could cause a spurious regression
problem. In that case, first differencing should be used to remove any
potential unit roots.

It is standard to assume zero contemporaneous correlation; i.e., that $%
u_{it} $ is uncorrelated with the number of wells drilled, or the wind
capacity installed at $t$. But what about the correlation between $u_{it}$
and, say, $\mathbf{x_{i,t+1}}$? Does future well drilling activity or
wind-farm construction depend on past shocks to the county's employment? We
do not believe that such feedback is important for our study, since almost
all energy produced is sold outside the county and total employment in a
county is not the main goal of energy companies. Therefore, it seems
reasonable to assume that past county employment across all industries has a
negligible effect on energy companies' future plans.

Another issue is that a correlation might exist between $u_{it}$ and past $%
\mathbf{x_{i,t-1}},\ldots \mathbf{x_{i,1}}$, leading to a failure of
sequential exogeneity. This would be the case if well-drilling activity and
wind farm construction have lasting effects on local employment. One way to
deal with this kind of correlation is to include lags of the explanatory
variables into the model. Strict exogeneity may then hold if enough lags are
included.\footnote{%
Another remedy is to use instrumental variables, but it is often difficult
to find suitable instruments.}

A test of strict exogeneity is based on \citet{Wooldridge2002}, 10.7.1. 
\begin{equation}
\Delta y_{it}=\mathbf{\Delta x_{it}\beta }+\mathbf{w_{it}\gamma }+\Delta
u_{it},t=2,\ldots ,T,  \label{eq:exogeitytest}
\end{equation}%
In the above equation, $w_{i,t}$ is a subset of $x_{i,t}$. Under strict
exogeneity, none of the $\mathbf{x_{it}}$s should be significant explanatory
variables in the first difference (FD) equation. That is, we should find
support of the hypothesis $H_{0}: \gamma =0$. Carrying out this test, the $F$
statistic on $\gamma $ is $0.32$, with $p$-value $0.5695$. Thus, we could
not reject $H_{0}$.

We have not ruled out serial correlation in the idiosyncratic error $u_{it}$%
, that is, $Corr(u_{it},u_{is})\neq 0$, $t\neq s$. Specifically, here we
will only consider serial correlation across time, excluding any
cross-sectional correlations a priori. If one allows for the $u_{it}$s to be
serially correlated over time, the usual pooled ordinary least squares (OLS)
and fixed effects (FE) standard errors are not valid, even asymptotically.
To test for the existence of serial correlation in the $u_{it}$s, we use the
Breusch-Godfrey/Wooldridge LM test and the Wooldridge first difference test %
\citep{Wooldridge2002}. Rather than interpreting serial correlation as a
technical violation of the OLS assumption, we rather take it as evidence of
dynamic responses. This leads us to consider including lagged dependent
variables on the right hand side.

Observe that the strict exogeneity assumption necessarily fails in models
with unobserved effects and lagged dependent variables. The reason is that $%
y_{it} $ is correlated with $u_{it}$ and would show up as part of
explanatory variables at $t+1$, implying that $E(u_{it}|\mathbf{x_{i,t+1}}%
)\neq 0$. Additional care is required when we include lagged dependent
variables as explanatory variables on the right hand side.

\subsection{The Finite Distributed Lag (FDL) Model}

\label{sec:finite-distr-lag} Since we expect that drilling and wind farm
construction can have lasting effects on local employment, we include lags
of explanatory variables into the model. A finite distributed lag model is
appropriate if the impact of the explanatory variables lasts over a finite
number of periods, $q$, and then stops. The FDL unobserved effects model
expands equation \eqref{eq:basic} to the following form: 
\begin{equation}
{E}_{it}=\sum_{k=0}^{q}\beta _{k}{wells}_{i,t-k}+\sum_{k=0}^{q}\delta _{k}{%
wcap}_{i,t-k}+c_{i}+\theta _{t}+u_{it}  \label{eq:FDL}
\end{equation}%
where $E_{it}$ denotes total employment, $wells_{it}$ denotes the number of
directional/fractured wells drilled, and $wcap_{it}$ indicates wind capacity
installed, in county $i=1,2,\ldots ,254$ and month $t=1,2,\ldots ,T$. Our
interest lies in the pattern of coefficients $\{ \beta _{k},\delta
_{k}\}_{k=0}^{q}$. The values of $\beta _{0}$ and $\delta _{0}$ capture the
immediate change in $E_{i}$ due to the one-unit increase in $wells_{i}$ and $%
wcap_{i}$, respectively, at time $t$. Similarly, $\beta _{k}$ and $\delta
_{k}$ capture the changes in $E_{i}$, $k$ periods after the new activity. At
time $t+q$, $E_{i}$ has reverted back to its initial level, $%
E_{i,t+q}=E_{i,t-1}$.

\subsection{The Autoregressive Distributed Lag (ADL) Model}

\label{sec:autore-distr-lag}

We are also interested in allowing for a long-lasting change in $E_{i}$ in
response to a change in any of the explanatory variables.\footnote{%
The right lag length is rarely known in advance, or pinned down by theory.}
In principle, one could do this by allowing for a large number of lags of
the explanatory variables. In practice, however, the inclusion of many
lagged variables will reduce degrees of freedom. In addition, the fact that
the resulting explanatory variables are likely to be correlated might lead
to severe multicollinearity.

One way of bypassing the multicollinearity problem is by including one or
more lags of the dependent variable. The model becomes an autoregressive
distributed lag (ADL) model. The ADL model is similar to the FDL model,
except that the impact of explanatory variables persists over time at a
geometrically declining rate. Denoting the number of lagged dependent
variables by $p$, an ADL$(p,q)$ model with unobserved effects has the form: 
\begin{equation}
{E}_{it}=\sum_{j=1}^{p}\lambda _{j}E_{i,t-j}+\sum_{k=0}^{q}\beta _{k}{wells}%
_{i,t-k}+\sum_{k=0}^{q}\delta _{k}{wcap}_{i,t-k}+c_{i}+\theta _{t}+u_{it}
\label{eq:ADL}
\end{equation}%
where $\{ \lambda _{j}\}_{j=1}^{p}$ are the autoregressive coefficients.
Following a temporary change in $wells$ equal to 1, $E_{it}$ will initially
go up by $\beta _{0}$ in period 1, then by $\beta _{1}+\lambda _{1}\beta _{0}
$ in period 2, by $\beta _{2}+\lambda _{1}(\beta _{1}+\lambda _{1}\beta
_{0})+\lambda _{2}\beta _{0}$ in period 3... etc. Provided that the process
is stationary, the ADL model eventually reaches a new equilibrium employment
that is 
\begin{equation}
\frac{\sum_{k=0}^{q}\beta _{k}}{1-\sum_{j=0}^{p}\lambda _{j}}
\label{eq:LRP_pq}
\end{equation}%
higher than the original equilibrium.

Another advantage of the ADL model is that the inclusion of a lagged
dependent variable can often eliminate serial correlation, particularly if
additional lags of the dependent variable are included. Lags of the
independent variables may also eliminate serial correlation in the error
term.\footnote{%
An interpretation is that the serial correlation is present in the simple
model because that model ignores the dynamic adjustment process. If that is
correctly specified, the serial correlation disappears.} Hence, once we
introduce lagged values of $y_{it}$, a correct dynamic specification implies
sequential exogeneity. However, the strict exogeneity assumption is false,
as we discussed above. In this case, both the fixed effects (FE) estimator
and the first difference (FD) estimator are inconsistent.\footnote{%
Deciding which model to use and how many lags to include is complicated by
the fact that we are unlikely to have a theory to distinguish between the
different models. As a result, \citet*{Boef2008} and others have advocated
starting with a general model like the ADL and testing down to a more
specific model, including the optimal values for $p$ and $q$.}

\subsection{The Spatial Panel Model}

\label{sec:spatial-panel-models}In this section, we discuss cross-sectional
dependence (XSD) in panels. This can arise, for example, if spatial
diffusion processes are present causing different panel members to be
related. In our case, shale or wind farm activity in one county could affect
employment in neighboring counties. Spatial interaction effects could be due
to competition or complementarity between counties, spillovers,
externalities, regional correlations in industry structures, or shocks
affecting similar industries (for example, weather shocks affecting
agriculture in both counties), as well as many other factors.

The $CD$ and $CD(p)$ tests \citep{Pesaran2004} are used to detect XSD%
%TCIMACRO{\TeXButton{@}{\@}}%
%BeginExpansion
\@%
%EndExpansion
. These tests are based on the averages over the time dimension of pairwise
correlation coefficients for each pair of cross-sectional units. The $CD(p)$
test also takes into account an appropriate subset of neighboring
cross-sectional units in order to check the null of no XSD against
dependence between neighbors only. To do so, a spatial weights matrix, $W$,
is needed for the $CD(p)$ test.

Matrix $W$ is a $254\times 254$ non-negative matrix, in which the element $%
w_{ij}$ expresses the degree of spatial proximity between the pair of
objects $i$ and $j$. Following \citet{Kapoor2007}, the diagonal elements $%
w_{ii}$ are all set to zero, to exclude self-neighbors. Furthermore, only
neighborhood effects are considered in this paper, that is, $W$ is a
contiguity matrix:\footnote{$W$ is also called as adjacency matrix.} 
\begin{equation}
w_{ij}=%
\begin{cases}
1,\text{ if $i$ and $j$ are neighbors} \\ 
0,\text{ otherwise}.%
\end{cases}
\label{eq:wmat}
\end{equation}
In our data set, both the $CD$ and $CD(p)$ tests (the latter with the above $%
W$ matrix) show the presence of XSD at 0.000 level.

We use a spatial panel model to allow for the spatial interaction across
counties. The contiguity matrix is transformed into row-standardized form,
which assumes that the impacts on a given county from all neighboring
counties are equal. Given a spatial weights matrix $W$, a family of related
spatial econometric models can be expanded from equation \eqref{eq:basic}: 
\begin{equation}
E_{it}=\rho \sum_{j=1}^{N}w_{ij}E_{jt}+\beta _{1}{wells}_{it}+\beta _{2}{wcap%
}_{it}+u_{it},  \label{eq:full}
\end{equation}%
where $\rho $ is the spatial autoregressive coefficient and $N$ is the
number of neighbors. We specify the composite error $u_{it}$ following %
\citet{Kapoor2007}. They assume that spatial correlation applies to both
unobserved individual effects and the remainder error components. In this
case, $u_{it}$ follows a first order spatial autoregressive process of the
form: 
\begin{equation}
u_{it}=\lambda \sum_{j=1}^{N}w_{ij}u_{jt}+\epsilon _{it}  \label{eq:u_rho}
\end{equation}%
and $\epsilon $ follows an error component structure 
\begin{equation}
\epsilon _{it}=c_{i}+\nu _{it}  \label{eq:epsilon_fixed}
\end{equation}%
to further allow $\epsilon _{it}$ to be correlated over time.

\section{Results}

\subsection{The FDL model}

\label{sec:estimation-fdl-model}

As noted earlier, including lagged dependent variables in the model violates
strict exogeneity, implying that the resulting autoregressive FD estimator
may suffer from asymptotic bias. Therefore, in this section we drop all
lagged dependent variables and use the FDL approach. We verified that the
strict exogeneity assumption holds as long as enough lags of the explanatory
variables are included.

To obtain a First-Difference (FD) estimator, lag the model in (\ref{eq:FDL})
by one period and subtract to obtain: 
\begin{equation}  \label{eq:FD}
\Delta E_{it}=\sum_{k=0}^{q}\beta _{k}\Delta
wells_{i,t-k}+\sum_{k=0}^{q}\delta _{k}\Delta wcap_{i,t-k}+\theta
_{0}+\theta _{t}+\Delta u_{it},\text{ }t=2,3,\ldots ,T
\end{equation}%
Note that rather than dropping an overall intercept and including the
differenced time dummies $\Delta \theta _{t}$, we estimated an intercept and
then included the time dummies $\theta _{t}$, for $T-2$ of the remaining
periods. Because the set of the regressors involving the time dummies are
non-singular linear transformations of each other, the estimated
coefficients on the other variables do not change.

Next, we test for the presence of serial correlation in $\Delta u_{it}$
using the Breusch-Godfrey and Wooldridge tests for serial correlation in
panels. Both tests reject $H_0$ and show serial correlation remains in the
idiosyncratic errors. I then increased $p$ up to $p = 36$. The results
showed that serial correlation remained no matter how many lags of the
explanatory variables were included. This serial correlation may imply that
the model does not fully capture the actual dynamic adjustment process.

We proceed by computing a robust variance matrix for the FD estimator, which
accommodates a fully general structure with respect to heteroskedasticity
and serial correlation in $\Delta u_{it}$. Following \citet{Arellano1987},
this robust variance matrix is consistent. To determine the appropriate lag
length, $q$, we posited a maintained value that should be larger than the
optimal $q$. Here we use $q=24$. We then performed sequential $F$ tests on
the last $24>p$ coefficients, stopping when the test rejects the $H_{0}$
that the coefficients are jointly zero at a 5\% level. Using a robust
variance matrix to calculate the $F$ statistics, we drop 18 lagged
explanatory variables and assign $q=6$.

\begin{table}[h]
\centering
\begin{tabular}{llll}
\hline \hline
\multicolumn{1}{l}{\textbf{Variable}} & {\textbf{Coefficient}} & \textbf{%
(Std. Err.)} & \textbf{(Robust SE.)} \\ \hline
$wells_{it}$ & 16.31 & (5.396)$^{**}$ & [6.06]$^{**}$ \\ 
$wells_{i,t-1}$ & 13.17 & (6.666)$^{*}$ & [7.081]$^{.}$ \\ 
$wells_{i,t-2}$ & 0.932 & (7.025) & [2.929] \\ 
$wells_{i,t-3}$ & -5.519 & (7.127) & [6.006] \\ 
$wells_{i,t-4}$ & 12.23 & (7.119)$^{.}$ & [8.705] \\ 
$wells_{i,t-5}$ & 17.89 & (6.875)$^{**}$ & [11.13] \\ 
$wells_{i,t-6}$ & 22.46 & (5.686)$^{***}$ & [12.91]$^{.}$ \\ 
$wcap_{it}$ & -0.756 & (1.235) & [0.923] \\ 
$wcap_{i,t-1}$ & -0.755 & (1.653) & [0.594] \\ 
$wcap_{i,t-2}$ & -0.739 & (1.864) & [0.332]$^{*}$ \\ 
$wcap_{i,t-3}$ & -0.212 & (1.923) & [0.323] \\ 
$wcap_{i,t-4}$ & 0.111 & (1.865) & [0.374] \\ 
$wcap_{i,t-5}$ & 0.250 & (1.654) & [0.432] \\ 
$wcap_{i,t-6}$ & -0.178 & (1.236) & [0.266] \\ \hline
\multicolumn{4}{l}{\textsuperscript{***}$p<0.001$, \textsuperscript{**}$%
p<0.01$, \textsuperscript{*}$p<0.05$, \textsuperscript{$\cdot$}$p<0.1$} \\ 
\hline \hline
\end{tabular}%
\caption{FD Estimation Results, $q=6$}
\label{table:FD_results}
\end{table}

The estimation results are reported in Table~\ref{table:FD_results}, with
both robust standard errors and the usual FD standard errors.\footnote{%
Note that $R^{2}=0.00084$. Since oil and gas-related employment is only
2.6\% of the total employment in Texas, a low explanatory power of the
regression model is perhaps to be expected.} Using robust standard errors,
we find that five out of seven coefficients of the wind installed capacity
are negative and all but one are statistically insignificantly different
from zero.\footnote{%
The order 2 lag is negative and statistically significantly different from
zero at a 5\% level.} A joint $F$-test on $H_{0}:\delta _{k}=0$ for $%
k=0,1,\ldots ,6$ gives $F(7,31734)=0.78$ with $p$-value $=0.6001$. Thus, we
cannot reject the hypothesis that the impact of wind activity on employment
is not statistically significantly different from zero. Using robust
standard errors we also find that all coefficients on the $wells$ variables
except for the contemporaneous one are not statistically significantly
different from zero. The contemporaneous one is significant at the better
than 1\% level. However, since there is substantial correlation in $wells$
at different lags (multicollinearity), it can be difficult to obtain precise
estimates for the individual $\beta $s. The estimated long run multiplier is
4.4. On the other hand, we found $wells_{t}$, $wells_{t-1}$,\ldots and $%
wells_{t-6}$ to be jointly significant: the $F$ statistic has a $p$-value
equal to $0.0007$. Adding the estimated coefficients of the current and
lagged variables, we obtain long-term multipliers $LRP_{wells}=77.46$.
Assuming that all the jobs created are short-term (they only last for 1
month), we divide $LRP_{wells}$ by 12 to obtain the number of full-time
equivalent (FTE) jobs: $6.42.$\footnote{%
This allows us to avoid re-counting the same person working on the same job
for more than one month as two different new jobs.} Given that 5482 new
directional/fractured wells were drilled in Texas in 2011, the estimates
imply that about 35000 FTE jobs would have been created.\footnote{%
The total employment in Texas in 2010 was $10,182,150$.}

\begin{figure}[h]
\centering
\subfloat[]{\label{fig:fdwells6}\includegraphics[width=0.48%
\textwidth]{fdwells6.pdf}} \subfloat[]{\label{fig:fdwind6}%
\includegraphics[width=0.48\textwidth]{fdwind6.pdf}}
\caption[FD estimation results with 6 lags]{FD estimation results with $q =
6 $: (a) wells (b) wind capacity}
\label{fig:FDlag6full}
\end{figure}

We graph the point estimates of the short-run impact of $wells_{k}$ and $%
wcap_{k}$ as a function of $k$ in Figure~\ref{fig:FDlag6full}. The lag
distribution summarizes the dynamic effects from a temporary increase in the
explanatory variables on the dependent variable.

Figure~\ref{fig:fdwells6} shows a mainly declining trend in the impact of
wells in the first three months. This may be because workers leave after the
well completion. Employment then increases starting with month $4$. This
could be the result of the emergence of new business opportunities in the
area, resulting from the well-drilling activity. We find that the largest
effect is with the first and the last lag.

Figure~\ref{fig:fdwind6} shows the impact from the added new wind capacity.
The employment effect is estimated to be negative at first, and then
increase, peaking about five months after the wind farm construction. In
reality, however, given the estimated standard errors we cannot really say
there is convincing evidence of any relationship between wind farm
construction and county employment.

\subsection{The ADL model}

\label{sec:adl-dynamic-model} Since the ADL model involves lagged dependent
variables, the strict exogeneity assumption is violated and both the FE and
the FD estimators are inconsistent.\footnote{%
If we maintain the contemporaneous exogeneity assumption, the FE estimator's
inconsistency shrinks to zero at the rate $1/T$, while the inconsistency of
the FD estimator is essentially independent of $T$ (Wooldridge, 2002).} To
overcome this, we use a generalized method of moments (GMM) estimator.

We again need to assign appropriate $p$ and $q$ to the model before we
estimate it. When we include one lagged dependent variable, $E_{i,t-1}$ (so $%
p=1$), Wooldridge's test for serial correlation gives $\chi ^{2}=30.189$,
with $p$-value $=3.919e^{-8}$. The strong serial correlation implies that
the dynamic data generation processes has not been fully captured. When we
include one more lagged dependent variable, $E_{i,t-2}$, the test result
changes to $\chi ^{2}=0.0081$, with $p$-value $=0.9285$. We conclude that
the error term, $u_{it}$, is now serially uncorrelated. Henceforth, we set $%
p=2$.

As in the previous section we begin by setting $q=6$. We then proceed to
estimate the two-way Arellano-Bond GMM regression. The full results are
shown in Table~\ref{table:GMM_results} in Appendix~\ref{app:C}. Both the
Wald and the joint $F$ tests cannot reject that the coefficients for wind
capacity $\delta _{0}=\ldots =\delta _{6}=0$. Thus, we conclude that there
is a statistically significant impact of wind activity on local employment.
On the other hand, we again do not find any statistically significant effect
of wind farm construction on county employment. Figure~\ref{fig:GMMfull}
graphs the point estimate of the dynamic response of employment to a unit
increase in $wells_{it}$ and $wcap_{it}$ under six lags. In the case of $%
wells$, for example, the employment variable, $E_{it}$, initially increases
by $\beta _{0}$ in period 1, then by $\beta _{1}+\lambda _{1}\beta _{0}$ in
period 2, by $\beta _{2}+\lambda _{1}(\beta _{1}+\lambda _{1}\beta
_{0})+\lambda _{2}\beta _{0}$ in period 3,... etc.\footnote{%
The long-run effect, referred to as the long-run propensity (LRP) or
long-run multiplier, is given by $\sum_{k=0}^{19}\beta _{k}/(1-\lambda
_{1}-\lambda _{2})=121.13$, which is very close to the one estimated from
the FDL model using FGLS. Full results appear in the Appendix.} It is
noteworthy that these response patterns are quite similar to the ones found
for the ADL estimates and graphed in figure~\ref{fig:FDlag6full}.

\begin{figure}[h]
\centering
\subfloat[]{\label{fig:gmmwells}\includegraphics[width=0.48%
\textwidth]{gmmwells6.pdf}} \subfloat[]{\label{fig:gmmwind}%
\includegraphics[width=0.48\textwidth]{gmmwind6.pdf}}
\caption[GMM estimation results, $p=2$, $q=6$]{GMM estimation results with $%
p=2$, $q=6$: (a) wells (b) wind capacity}
\label{fig:GMMfull}
\end{figure}

The sum of the two estimated coefficients for the lagged dependent variables
is $0.98$. Although the test $\lambda _{1}+\lambda _{2}=1$ is rejected at
the 1\% level, employment might still follow a unit root process. To address
this issue, we ran the same estimation using data from only the 98 counties
with stationary employment series. We obtained similar estimates: $\hat{%
\lambda _{1}}+\hat{\lambda _{2}}=0.98$. We believe that the large
persistence in employment is probably due to the small explanatory power of
well drilling. Since employment in the shale gas sector is a rather small
component of total employment, most of the systematic component of
employment variation would appear in the error term. It is not surprising
that this error is highly serially correlated.

\subsection{Spatial Panel Models}

\label{sec:spatial-panel-model} Including the lagged dependent variable as a
regressor in the spatial autoregression (SAR) model introduces simultaneity
bias and the OLS estimator is no longer unbiased and consistent. Including
the lagged dependent variable the spatial error model (SEM) yields an OLS
estimator that is unbiased, but inefficient. Therefore, maximum likelihood
estimation is used to estimate the parameters of both models.

Both the SAR and SEM models are estimated allowing for two-way fixed
effects. The results are reported in Table~\ref{tab:sp_effects_emp} in
Appendix~\ref{app:C}. We find that the spatial interaction coefficients of
both models are statistically significantly different from zero and very
similar: $\rho =0.1730$, $\lambda =0.1734$. Also, both models show large and
statistically significant coefficients for $wells$, and coefficients for $%
wcap$ that are not statistically significantly different from zero.

Following \citet{LeSage2009}, the expectation of the dependent variable $y$
in the SAR model $y=\rho Wy+X\beta +\epsilon $ is 
\begin{equation}
E(y)=(I_{N}-\rho W)^{-1}X\beta  \label{eq:SAR}
\end{equation}%
Employment in county $i$ depends on developments in neighboring counties, as
a result of the various spatial spillover effects discussed previously.

The own- and cross-partial derivatives in the SAR model take the form of an $%
N\times N$ matrix that can be expressed as: 
\begin{equation}
\partial y/\partial x_{r}^{\prime }=(I_{N}-\rho W)^{-1}I_{N}\beta _{r}
\label{eq:SARpartial}
\end{equation}%
These partial derivatives measure how drilling/wind activities in county $j$
influence employment in county $i$. For the $r$th explanatory variable, the
average of the main diagonal elements of this matrix is labeled as the
\textquotedblleft direct effect.\textquotedblright \ It measures how wells
drilled in a particular county affect employment in that same county. The
average of the cumulative off-diagonal elements over all observations
corresponds to the \textquotedblleft indirect\textquotedblright \ or
spillover effect. The average total effect will be the sum of the two. We
use equation (\ref{eq:SARpartial}) to calculate both the direct and the
indirect effects resulting from well-drilling activity.

The SAR model implies that direct effect of well-drilling activity on
employment is $225$, and it is significant at the 0.000 level. The result
shows that about 225 jobs would be created per well drilled in the same
county. The estimated indirect effect of well drilling activity is 46, which
increases the total effect to 271. Thus, if we only consider the direct
effect, the results would be underestimated by $17\%$. The result is
significantly higher compared to the FD estimation results, $LRP=77$,
indicating that spatial correlation effects are important.

Similarly, the estimated direct and indirect effects of wind activity are
0.05 and 0.01, respectively, but neither coefficient is statistically
significantly different from zero. Hence, wind farm installation and
construction was not found to have an impact on total county employment in
these data.

\subsection{Wage Effects}

\label{sec:estim-results-wage} In this section, examine whether there is any
evidence in our data set for shale gas and wind developments affecting
average weekly wages. We again employ the FD approach. Sequential F-tests
determine that $q=12$. The results appear in Table~\ref{table:FDwage} in
Appendix~\ref{app:C}. The results show that the coefficients of the $4^{th}$
and $9^{th}$ lagged wells are statistically significantly different from
zero at the 0.05 level. For wind capacity, the coefficients of lag $1$ and
lag $10$ are statistically significantly different from zero at the 0.05
level, while coefficients on lags 11 and 12 are statistically significantly
different from zero at the 0.01 level.

Figure~\ref{fig:fdwage} graphs the resulting dynamic response of wages to a
unit increase in $wells_{it}$ and $wcap_{it}$ (12 lags). The impact from
wells drilled rises and falls with a 6 month cycle. The peaked value is
about 0.3. The impact from wind capacity installation shows a quite
different trend: it increases over time from near zero to 0.13.

\begin{figure}[h]
\centering \includegraphics[width=0.7\textwidth]{fdwage.pdf}
\caption{Short run impact of shale/wind activity on wage}
\label{fig:fdwage}
\end{figure}
The spatial panel regression results for wages are shown in Table~\ref%
{tab:sp_effects_wage} in Appendix~\ref{app:C}. The results are in line with
those without including spatial interaction effects. According to the SAR
model, the estimate of $\beta _{1}$ is 0.18 and statistically significantly
different from zero at the 0.05 level, while the estimate of $\beta _{2}$ is
0.06 and not statistically significantly different from zero. Additionally,
the results show a strong spatial correlation: $\rho =\lambda =0.26$.
Interestingly, the spatial correlation effects in wages are even larger than
in employment: $26\%$ of the increase in average wages is due to indirect
effects, while we found such effects are responsible for only $17\%$ of the
change in employment.

The direct and indirect effects of well drilling activity on wages are 0.18
and 0.06, respectively. Alternatively, the total effect on wages is 23 cents
per well drilled, of which 18 cents are due to drilling activity in the same
county, while 6 cents are attributed to drilling activity in neighboring
counties. The total effect from wind activity is 8 cents per MW, of which
about 6 cents are due to the direct effect, while 2 cents are due to the
indirect effect.

\section{Conclusion}

\label{sec:conclusions} We followed an econometric approach to compare job
creation in wind power versus that in the shale gas sector. We have
discussed the advantages and disadvantages of a number of different models.
We then estimated them using county level data in Texas from 2001 to 2011.
The results were quite consistent. Both first-difference and GMM methods
show that shale development and well-drilling activity have brought strong
employment to Texas: 77 short-term jobs or 6.4 FTE jobs per well. Given that
5482 new directional/fractured wells were drilled in Texas in 2011, an
estimated 35000 FTE jobs were created. In contrast, we did not find a large
effect on wages. The effect on wages corresponds to a 30-cent increase in
month 4 and month 9 after each well completion.

All our estimations show that the impact from wind industry development on
employment is not significantly different from zero. Its impact on wages
increases gradually after construction and peaks about one year later. We
found that 13 cents are added to wages in months 10 to 12 after construction.

While this study by no means examines all, or even the most important,
economic issues surrounding the development of wind farms, the effects on
employment in Texas counties over the period examined appear to be
insignificant. By contrast, unconventional gas production appears to be a
significant force behind employment growth in Texas counties in this period.
Further research is needed to confirm these effects in other states and
other time periods.

\clearpage
\appendix

\section{Tables of Estimation Results}

\label{app:C}

\begin{table}[h]
\centering
\begin{tabular}{lll}
\hline \hline
\multicolumn{1}{l}{\textbf{Variable}} & {\textbf{Coefficient}} & \textbf{%
(Std. Err.)} \\ \hline
$E_{i,t-1}$ & 0.88241 & 0.0056054$^{***}$ \\ 
$E_{i,t-2}$ & 0.1005518 & 0.0056067$^{***}$ \\ 
$wells_{it}$ & 15.86961 & 5.586163$^{**}$ \\ 
$wells_{i,t-1}$ & -.8539942 & 5.714926 \\ 
$wells_{i,t-2}$ & -15.90933 & 5.853195$^{**}$ \\ 
$wells_{i,t-3}$ & -8.917589 & 5.964613 \\ 
$wells_{i,t-4}$ & 14.38985 & 5.922299 $^{*}$ \\ 
$wells_{i,t-5}$ & 4.476252 & 5.851388 \\ 
$wells_{i,t-6}$ & 3.622863 & 5.794333 \\ \hline
$wcap_{it}$ & -9.00e-06 & 0.0000248 \\ 
$wcap_{i,t-1}$ & -0.0000184 & 0.0000243 \\ 
$wcap_{i,t-2}$ & 0.0000239 & 0.0000237 \\ 
$wcap_{i,t-3}$ & 0.0000107 & 0.0000236 \\ 
$wcap_{i,t-4}$ & 0.0000242 & 0.0000237 \\ 
$wcap_{i,t-5}$ & -0.0000129 & 0.0000243 \\ 
$wcap_{i,t-6}$ & -9.63e-06 & 0.0000249 \\ \hline
\multicolumn{3}{l}{Signif. Code: \textsuperscript{***} 0, %
\textsuperscript{**} 0.01, \textsuperscript{*} 0.05, \textsuperscript{.} 0.1,%
$\;$ 1.} \\ \hline \hline
\end{tabular}%
\caption{GMM Estimation Results, $p = 2$, $q = 6$}
\label{table:GMM_results}
\end{table}

\begin{table}[h]
\centering
\begin{tabular}{|l|c|c|c|c|}
\hline
\multicolumn{5}{|l|}{SAR Coefficients:} \\ \hline
& Estimate & Std. Error & t-value & Pr$(>|t|)$ \\ \hline
$\rho$ & 0.1730 & 0.0081 & 21.43 & $<2e-16^{***}$ \\ 
wells & 224.72 & 12.99 & 17.29 & $<2e-16^{***}$ \\ 
newcap & 0.05 & 6.366 & 0.0079 & 0.9937 \\ \hline
\multicolumn{5}{|l|}{SEM Coefficients:} \\ \hline
$\lambda$ & 0.1734 & 0.0081 & 21.42 & $<2e-16^{***}$ \\ 
wells & 235.81 & 13.63 & 17.30 & $<2e-16^{***}$ \\ 
newcap & 0.47 & 6.374 & 0.704 & 0.4814 \\ \hline
\multicolumn{5}{|l|}{Significant Code: \textsuperscript{***} 0, %
\textsuperscript{**} 0.01, \textsuperscript{*} 0.05, \textsuperscript{.} 0.1,%
$\;$ 1.} \\ \hline
\end{tabular}%
\caption{Spatial interaction effects on employment}
\label{tab:sp_effects_emp}
\end{table}

\begin{table}[h]
\centering
\begin{tabular}{|l|c|c|c|c|}
\hline
\multicolumn{5}{|l|}{SAR Coefficients:} \\ \hline
& Estimate & Std. Error & t-value & Pr$(>|t|)$ \\ \hline
$\rho$ & 0.26 & 0.01 & 33.88 & $<2e-16^{***}$ \\ 
wells & 0.18 & 0.09 & 2.04 & $0.0412^{*}$ \\ 
newcap & 0.06 & 0.04 & 1.51 & 0.1319 \\ \hline
\multicolumn{5}{|l|}{SEM Coefficients:} \\ \hline
$\lambda$ & 0.26 & 0.01 & 33.87 & $<2e-16^{***}$ \\ 
wells & 0.12 & 0.09 & 1.27 & 0.20 \\ 
newcap & 0.07 & 0.04 & 1.60 & 0.11 \\ \hline
\multicolumn{5}{|l|}{Significant Code: \textsuperscript{***} 0, %
\textsuperscript{**} 0.01, \textsuperscript{*} 0.05, \textsuperscript{.} 0.1,%
$\;$ 1.} \\ \hline
\end{tabular}%
\caption{Spatial interaction effects on wage}
\label{tab:sp_effects_wage}
\end{table}

\begin{table}[h]
\centering
\begin{tabular}{lllll}
\hline \hline
{\textbf{Variable}} & {\textbf{Coefficient}} & {\textbf{Robust SE.}} & 
\textbf{t-value} & \textbf{$Pr(>|t|)$} \\ \hline
$wells_{t}$ & 0.030579 & 0.112459 & 0.2719 & 0.785688 \\ 
$wells_{t-1}$ & -0.013512 & 0.149363 & -0.0905 & 0.927917 \\ 
$wells_{t-2}$ & 0.105605 & 0.160727 & 0.6570 & 0.511159 \\ 
$wells_{t-3}$ & 0.128526 & 0.159170 & 0.8075 & 0.419399 \\ 
$wells_{t-4}$ & 0.310342 & 0.132325 & 2.3453 & 0.019018 * \\ 
$wells_{t-5}$ & 0.040933 & 0.119525 & 0.3425 & 0.732003 \\ 
$wells_{t-6}$ & -0.022280 & 0.128208 & -0.1738 & 0.862040 \\ 
$wells_{t-7}$ & 0.057849 & 0.121926 & 0.4745 & 0.635176 \\ 
$wells_{t-8}$ & 0.268792 & 0.137164 & 1.9596 & 0.050047 . \\ 
$wells_{t-9}$ & 0.297772 & 0.133959 & 2.2229 & 0.026232 * \\ 
$wells_{t-10}$ & 0.180401 & 0.108571 & 1.6616 & 0.096603 . \\ 
$wells_{t-11}$ & -0.121860 & 0.155113 & -0.7856 & 0.432095 \\ 
$wells_{t-12}$ & -0.014441 & 0.144573 & -0.0999 & 0.920434 \\ 
$wcap_{t}$ & -0.020312 & 0.022022 & -0.9224 & 0.356349 \\ 
$wcap_{t-1}$ & -0.079381 & 0.039836 & -1.9927 & 0.046304 * \\ 
$wcap_{t-2}$ & -0.075806 & 0.057576 & -1.3166 & 0.187978 \\ 
$wcap_{t-3}$ & -0.064414 & 0.053879 & -1.1955 & 0.231889 \\ 
$wcap_{t-4}$ & -0.089130 & 0.058455 & -1.5248 & 0.127325 \\ 
$wcap_{t-5}$ & -0.053716 & 0.051733 & -1.0383 & 0.299123 \\ 
$wcap_{t-6}$ & -0.017880 & 0.041650 & -0.4293 & 0.667710 \\ 
$wcap_{t-7}$ & -0.022297 & 0.065829 & -0.3387 & 0.734828 \\ 
$wcap_{t-8}$ & 0.004571 & 0.056724 & 0.0806 & 0.935773 \\ 
$wcap_{t-9}$ & 0.025598 & 0.058720 & 0.4359 & 0.662890 \\ 
$wcap_{t-10}$ & 0.134719 & 0.058776 & 2.2921 & 0.021907 * \\ 
$wcap_{t-11}$ & 0.136272 & 0.042045 & 3.2411 & 0.001192 ** \\ 
$wcap_{t-12}$ & 0.102829 & 0.035770 & 2.8748 & 0.004046 ** \\ \hline
\multicolumn{5}{l}{Significant Code: \textsuperscript{***} 0, %
\textsuperscript{**} 0.01, \textsuperscript{*} 0.05, \textsuperscript{.} 0.1,%
$\;$ 1.} \\ \hline \hline
\end{tabular}%
\caption{FD estimation results with robust se. on wage, $q = 12$}
\label{table:FDwage}
\end{table}
\clearpage
\bibliographystyle{plain}
\bibliography{EnergyEcon}

\end{document}
